Installation

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Installation
Learn how to install PyCaret
Help us improve the documentation! If you found a broken link or a typo or would like to contribute to this documentation, please submit a pull request on pycaret-docs repo.
Install
PyCaret is tested and supported on the following 64-bit systems:
Python 3.6 – 3.8
Python 3.9 for Ubuntu only
Ubuntu 16.04 or later
Windows 7 or later
Install PyCaret with Python's pip package manager.
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pip install pycaret
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To install the full version (see dependencies below):
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pip install pycaret[full]
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If you want to try our nightly build (unstable) you can install pycaret-nightly from pip. pip install pycaret-nightly
Environment
In order to avoid potential conflicts with other packages, it is strongly recommended to use a virtual environment, e.g. python3 virtualenv (see python3 virtualenv documentation) or conda environments. Using an isolated environment makes it possible to install a specific version of pycaret and its dependencies independently of any previously installed Python packages. 
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# create a conda environment
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conda create --name yourenvname python=3.8
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​
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# activate conda environment
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conda activate yourenvname
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​
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# install pycaret
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pip install pycaret
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​
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# create notebook kernel
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python -m ipykernel install --user --name yourenvname --display-name "display-name"
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PyCaret is not yet compatible with sklearn>=0.23.2.
GPU
With PyCaret, you can train models on GPU and speed up your workflow by 10x. To train models on GPU simply pass use_gpu = True in the setup function. There is no change in the use of the API, however, in some cases, additional libraries have to be installed as they are not installed with the default version or the full version. As of the latest release, the following models can be trained on GPU:
Extreme Gradient Boosting (requires no further installation)
Catboost (requires no further installation)
Light Gradient Boosting Machine requires GPU installation​
Logistic Regression, Ridge Classifier, Random Forest, K Neighbors Classifier, K Neighbors Regressor, Support Vector Machine, Linear Regression, Ridge Regression, Lasso Regression requires cuML >= 0.15​
Dependencies
Default dependencies that are installed with pip install pycaret are listed here.
Optional dependencies that are installed with pycaret[full] are listed here.
Testing requirements are listed here.
Select the tab
requirements
requirements-optional
requirements-test
pandas 
scipy<=1.5.4 
seaborn 
matplotlib 
IPython 
joblib 
scikit-learn==0.23.2 
ipywidgets 
yellowbrick>=1.0.1 
lightgbm>=2.3.1 
plotly>=4.4.1 
wordcloud 
textblob 
cufflinks>=0.17.0 
umap-learn 
pyLDAvis 
gensim<4.0.0 
spacy<2.4.0 
nltk 
mlxtend>=0.17.0 
pyod 
pandas-profiling>=2.8.0 
kmodes>=0.10.1 
mlflow 
imbalanced-learn==0.7.0 
scikit-plot 
Boruta 
pyyaml<6.0.0 
numba<0.55
shap 
interpret<=0.2.4 
tune-sklearn>=0.2.1 
ray[tune]>=1.0.0 
hyperopt 
optuna>=2.2.0 
scikit-optimize>=0.8.1 
psutil 
catboost>=0.23.2 
xgboost>=1.1.0 
explainerdashboard 
m2cgen 
evidently 
autoviz 
fairlearn 
fastapi 
uvicorn 
gradio 
fugue>=0.6.5 
boto3 
azure-storage-blob 
google-cloud-storage
pytest 
moto 
codecov 
NOTE: We are actively working on reducing default dependencies in the next major release. We intend to support functionality level and module-specific install in the future. For example: pip install pycaret[nlp].
Building from source
To install the package directly from GitHub (latest source), use the following command:
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pip install git+https://github.com/pycaret/pycaret.git#egg=pycaret
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Don't forget to include the #egg=pycaret part to explicitly name the project, this way pip can track metadata for it without having to have run the setup.py script.
Run the tests:
To launch the test suite, run the following command from outside the source directory:
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pytest pycaret
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Docker
Docker uses containers to create virtual environments that isolate a PyCaret installation from the rest of the system. PyCaret docker comes pre-installed with a Notebook environment. that can share resources with its host machine (access directories, use the GPU, connect to the Internet, etc.). The PyCaret Docker images are tested for each release.
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docker run -p 8888:8888 pycaret/slim
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For docker image with full version:
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docker run -p 8888:8888 pycaret/full
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To learn more, check out this documentation.
Welcome to PyCaret
Next - GET STARTED
Quickstart
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